Bibliographic record
Abstract
Purpose The purpose of this paper is to establish if organisational factors are leading to a negative effect on ambulance personnel’s health. In recent years, frontline ambulance personnel have displayed a consistent high rate of sickness amongst healthcare workers within the National Health Service in the UK. Post-traumatic stress disorder (PTSD) has previously been cited, but organisational factors may be stressors to health. Design/methodology/approach A search of electronic databases MEDLINE EBSCO, MEDLINE OVID, MEDLINE PUBMED, AMED, CINAHL, Web of Science, Zetoc within the time period of 2000–2017 resulted in six mixed methods studies. Hand searching elicited one further study. The literature provided data on organisational and occupational stressors (excluding PTSD) relating to the health of 2,840 frontline ambulance workers in the UK, Australia, Norway, the Netherlands and Canada. The robust quantitative data were obtained from validated questionnaires using statistical analysis, whilst the mixed quality qualitative data elicited similar themes. Narrative synthesis was used to draw theories from the data. Findings Organisational factors such as low job autonomy, a lack of supervisor support and poor leadership are impacting on the health and well-being of frontline ambulance workers. This is intertwined with the occupational factors of daily operational demands, fatigue and enforced overtime, so organisational changes may have a wider impact on daily occupational issues. Originality/value The findings have possible implications for re-structuring organisational policies within the ambulance service to reduce staff sickness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".